ABM | Insight Maker

Model combining system dynamics and agent based modeling. Based on Prochaska's Transtheoretical Model of Behaviour Change. See also preceding SD Version IM-574

Clone of Clone of Smoking Cessation

Sergei Titov

WIP Combining SD and ABM Representations

Clone of Combined SD and ABM SIR Disease Dynamics

Olga Kolchyna

Modelling the effect of street trees

Jian Song

8

Three Agent Model of IM-14058 with Spatial awareness. Unconscious affective dynamics Josh Epstein's Agent Zero Book webpage Part II p.89 with spatial ABM. See next version at IM-15690

Fear Conditioning 3 Agents with Spatial Patches

Geoff McDonnell

3 өзіндік

Aigen Bekbolatkyzy

3

A new archetype, The Tyranny of Small Steps (TYST) has been observed. Explained through a system dynamics perspective, the archetypical behaviour TYST is an unwanted change to a system through a series of small activities that may be independent from one another. These activities are small enough not to be detected by the ‘surveillance’ within the system, but significant enough to encroach upon the “tolerance” zone of the system and compromise the integrity of the system. TYST is an unintentional process that is experienced within the system and made possible by the lack of transparency between an overarching level and a local level where the encroachment is taking place.

Reference:

Haraldsson, H. V., Sverdrup, H. U., Belyazid, S., Holmqvist, J. and Gramstad, R. C. J. (2008), The Tyranny of Small Steps: a reoccurring behaviour in management. Syst. Res., 25: 25–43. doi: 10.1002/sres.859

The Tyranny of small steps archetype (agent based)

H Haraldsson

3

WIP Combining SD and ABM Representations

Clone of Combined SD and ABM SIR Disease Dynamics

Frocks

Model combining system dynamics and agent based modeling. Based on Prochaska's Transtheoretical Model of Behaviour Change. See also preceding SD Version IM-574

Clone of Clone of Clone of Smoking Cessation

Luis R Matos

WIP Ideas for a hybrid budding SD plus ABM depression dynamics model

Hybrid Depression Dynamics Model

Geoff McDonnell

This model is a classic instance of an Erlang Queuing Process.

We have the entities:

- A population of cars which start off in a "crusing" state;

- At each cycle, according to a Poisson distribution defined by "Arrival Rate" (which can be a constant, a function of time, or a Converter to simulate peak hours), some cars transition to a "looking" for an empty space state.

- If a empty space is available (Parking Capacity > Count(FindState([cars population],[parked]))) then the State transitions to "Parked."

-The Cars stay "parked" according to a Normal distribution with Mean = Duration and SD = Duration / 4

- If the Car is in the state "Looking" for a period longer than "Willingness to Wait" then the state timeouts and transitions to impatient and immediately transitions to "Crusing" again.

The model is set to run for 24 hours and all times are given in hours (or fraction thereof)

WIP:

- Calculate the average waiting time;

- Calculate the servicing level, i.e., 1- (# of cars impatient)/(#cars looking)

A big THANK YOU to Scott Fortmann-Roe for helping setup the model's framework.

Clone of Estacionamento

Ray Madachy

21

Modélisation spatiale et multi-agents d'une épidémie. Avec trois classes d'individus: susceptibles (sains), infectés (malades et contagieux), et remis (sains et temporairement immunisés).

Traduit et adapté de

https://insightmaker.com/insight/2846/Agent-Based-Disease-Simulation

Épidémie Multi-Agents

France Caron

Model a board game

HW5.1

Jeremiah Krick

WIP Ideas for a hybrid budding SD plus ABM depression dynamics model

Clone of Hybrid Depression Dynamics Model

Warih Puspitasari

A simple Susceptible - Infected - Recovered disease as a stock and flow model.

@LinkedIn, Twitter, YouTube

SIR Disease Model

Gene Bellinger

7

From Schluter et al 2017 article A framework for mapping and comparing behavioural theories in models of social-ecological systems COMSeS2017 video. See also Balke and Gilbert 2014 JASSS article How do agents make decisions? (recommended by Kurt Kreuger U of S)

Clone of Modelling human behaviour (MoHuB)

Luis R Matos

A simple agent based foraging model. Consumer agents will move between fertile patches consuming them.

This insight is an element of the Agent Based Modeling learning module in Systems KeLE.

Clone of Foraging Model

Nathaniel Vala

This Agent-based Model was an idea of Christopher DICarlo "Disease Transmission with Agent Based Model' aims to present the COVID cases in Puerto Princesa City as of June 3, 2021

Insight author: Jolina Rosile Magbanua

Clone of Clone of ABM Model of COVID-19 in Puerto Princesa City

Rojean R. Rosales

An implementation of the classic Game of Life using agent based modeling.

Rules:

A live cell with less than two alive neighbors dies.
A live cell with more than three alive neighbors dies.
A dead cell with three neighbors becomes alive.

Clone of The Game of Life

Frocks

3бөлім Агент

Gauhar Mukhameddamin

Based on Nate Osgood's April 2014 Singapore Presentation Youtube video and Lyle Wallis material Gojii at DecisioTech See also Complex Decison Technologies IM as a more polished version

Clone of Complex Intervention Modeling Areas

sub cribed

WIP Combining SD and ABM Representations

Clone of Combined SD and ABM SIR Disease Dynamics

Dusan Pavlovic

Tutorial model of disease dynamics using ABM

Clone of Agent-Based Disease Dynamics

Rodrigo

This model is a classic instance of an Erlang Queuing Process.

We have the entities:

- A population of cars which start off in a "crusing" state;

- At each cycle, according to a Poisson distribution defined by "Arrival Rate" (which can be a constant, a function of time, or a Converter to simulate peak hours), some cars transition to a "looking" for an empty space state.

- If a empty space is available (Parking Capacity > Count(FindState([cars population],[parked]))) then the State transitions to "Parked."

-The Cars stay "parked" according to a Normal distribution with Mean = Duration and SD = Duration / 4

- If the Car is in the state "Looking" for a period longer than "Willingness to Wait" then the state timeouts and transitions to impatient and immediately transitions to "Crusing" again.

The model is set to run for 24 hours and all times are given in hours (or fraction thereof)

WIP:

- Calculate the average waiting time;

- Calculate the servicing level, i.e., 1- (# of cars impatient)/(#cars looking)

A big THANK YOU to Scott Fortmann-Roe for helping setup the model's framework.

Clone of Clone of Parking Lot Problem (WIP)

Lynard Misa

Will the ducks make it to the pond? or will the hawks swoop on them?

Duck v Hawk v3

Bruce Schneider

4

ABM Models